Novelty indicator for enhanced prioritization of predicted gene ontology annotations

Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and might contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new annotations are both effective and efficient, especially if compared with time-consuming trials of biological validation. To limit the possibility that these techniques predict obvious and trivial high-level features, and to help prioritize their results, we introduce a new element that can improve accuracy and relevance of the results of an annotation prediction and prioritization pipeline. We propose a novelty indicator able to state the level of “originality” of the annotations predicted for a specific gene to Gene Ontology (GO) terms. This indicator, joint with our previously introduced prediction steps, helps by prioritizing the most novel interesting annotations predicted. We performed an accurate biological functional analysis of the prioritized annotations predicted with high accuracy by our indicator and previously proposed methods. The relevance of our biological findings proves effectiveness and trustworthiness of our indicator and of its prioritization of predicted annotations

MYC up-regulation confers vulnerability to dual inhibition of CDK12 and CDK13 in high-risk Group 3 medulloblastoma

BackgroundMedulloblastoma (MB) is the most common cerebellar malignancy during childhood. Among MB, MYC-amplified Group 3 tumors display the worst prognosis. MYC is an oncogenic transcription factor currently thought to be undruggable. Nevertheless, targeting MYC-dependent processes (i.e. transcription and RNA processing regulation) represents a promising approach.MethodsWe have tested the sensitivity of MYC-driven Group 3 MB cells to a pool of transcription and splicing inhibitors that display a wide spectrum of targets. Among them, we focus on THZ531, an inhibitor of the transcriptional cyclin-dependent kinases (CDK) 12 and 13. High-throughput RNA-sequencing analyses followed by bioinformatics and functional analyses were carried out to elucidate the molecular mechanism(s) underlying the susceptibility of Group 3 MB to CDK12/13 chemical inhibition. Data from International Cancer Genome Consortium (ICGC) and other public databases were mined to evaluate the functional relevance of the cellular pathway/s affected by the treatment with THZ531 in Group 3 MB patients.ResultsWe found that pharmacological inhibition of CDK12/13 is highly selective for MYC-high Group 3 MB cells with respect to MYC-low MB cells. We identified a subset of genes enriched in functional terms related to the DNA damage response (DDR) that are up-regulated in Group 3 MB and repressed by CDK12/13 inhibition. Accordingly, MYC- and CDK12/13-dependent higher expression of DDR genes in Group 3 MB cells limits the toxic effects of endogenous DNA lesions in these cells. More importantly, chemical inhibition of CDK12/13 impaired the DDR and induced irreparable DNA damage exclusively in MYC-high Group 3 MB cells. The augmented sensitivity of MYC-high MB cells to CDK12/13 inhibition relies on the higher elongation rate of the RNA polymerase II in DDR genes. Lastly, combined treatments with THZ531 and DNA damage-inducing agents synergically suppressed viability of MYC-high Group 3 MB cells.ConclusionsOur study demonstrates that CDK12/13 activity represents an exploitable vulnerability in MYC-high Group 3 MB and may pave the ground for new therapeutic approaches for this high-risk brain tumor

Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia

Brain functional disruption and cognitive shortfalls as consequences of neurodegeneration are among the most investigated aspects in current clinical research. Traditionally, specific anatomical and behavioral traits have been associated with neurodegeneration, thus directly translatable in clinical terms. However, these qualitative traits, do not account for the extensive information flow breakdown within the functional brain network that deeply affect cognitive skills. Behavioural variant Frontotemporal Dementia (bvFTD) is a neurodegenerative disorder characterized by behavioral and executive functions disturbances. Deviations from the physiological cognitive functioning can be accurately inferred and modeled from functional connectivity alterations. Although the need for unbiased metrics is still an open issue in imaging studies, the graph-theory approach applied to neuroimaging techniques is becoming popular in the study of brain dysfunction. In this work, we assessed the global connectivity and topological alterations among brain regions in bvFTD patients using a minimum spanning tree (MST) based analysis of resting state functional MRI (rs-fMRI) data. Whilst several graph theoretical methods require arbitrary criteria (including the choice of network construction thresholds and weight normalization methods), MST is an unambiguous modeling solution, ensuring accuracy, robustness, and reproducibility. MST networks of 116 regions of interest (ROIs) were built on wavelet correlation matrices, extracted from 41 bvFTD patients and 39 healthy controls (HC). We observed a global fragmentation of the functional network backbone with severe disruption of information-flow highways. Frontotemporal areas were less compact, more isolated, and concentrated in less integrated structures, respect to healthy subjects. Our results reflected such complex breakdown of the frontal and temporal areas at both intra-regional and long-range connections. Our findings highlighted that MST, in conjunction with rs-fMRI data, was an effective method for quantifying and detecting functional brain network impairments, leading to characteristic bvFTD cognitive, social, and executive functions disorders

Sem best shortest paths for the characterization of differentially expressed genes

In the last years, systems and computational biology focused their efforts in uncovering the causal relationships among the observable perturbations of gene regulatory networks and human diseases. This problem becomes even more challenging when network models and algorithms have to take into account slightly significant effects, caused by often peripheral or unknown genes that cooperatively cause the observed diseased phenotype. Many solutions, from community and pathway analysis to information flow simulation, have been proposed, with the aim of reproducing biological regulatory networks and cascades, directly from empirical data as gene expression microarray data. In this contribute, we propose a methodology to evaluate the most important shortest paths between differentially expressed genes in biological interaction networks, with absolutely no need of user-defined parameters or heuristic rules, enabling a free-of-bias discovery and overcoming common issues affecting the most recent network-based algorithms

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD)